# Snowplow Signals is now generally available
> Snowplow Signals is now generally available.
> Source: https://docs.snowplow.io/release-notes/snowplow-signals-is-now-generally-available/
> Documentation index: https://docs.snowplow.io/llms.txt
> Date: 2025-09-30
> Components: Signals

Snowplow Signals is now generally available. Signals computes and serves real-time customer context: attributes calculated from behavioral data, served to your applications and agents. With Signals, you can power in-session personalization, recommendations engines, adaptive UIs, and agentic experiences like AI copilots and chatbots — without spending months or years building and maintaining the underlying infrastructure yourself.

The new release includes:

### Multi-cloud and multi-warehouse support

- **Cloud platforms**: AWS and GCP deployments with identical developer experience
- **Data warehouses**: Native integration with Snowflake and BigQuery for batch processing (Databricks support coming soon)

### No-code UI in Snowplow Console

Product managers and engineers can manage Signals directly in the Snowplow UI without writing code. This includes:

- Self-service deployment flows
- User attribute management interface
- Configuration and monitoring tools

The Python SDK is available for teams that prefer code-based workflows.

![Attribute group editor in Console, configuring an attribute key, time to live, and a list of attributes with their event filters and aggregations](/assets/images/image-1-9c223b11713144e835bf4964ec4ecabc.png)

### Production-grade performance

The Profiles API delivers 45ms p50 response times, enabling in-session personalization use cases like:

- Dynamic pricing and offers
- Contextual product recommendations
- AI agent responses with real-time user context

### Live debugging with Snowplow Inspector

Debug and validate your personalization logic in real time using the Snowplow Inspector integration.

### Components

Signals includes several core components:

![Diagram of the Signals components: the streaming engine and batch engine calculate attributes that the Profiles API serves back to a website or app](/assets/images/image-2-6c26d8996e88c3fc04fb961cbe8c36b7.png)

- **Streaming engine**: Calculates real-time user attributes from live event streams (for example, current page views or cart status)
- **Batch engine**: Computes historical user attributes from warehouse data using ML models (for example, lifecycle stage or propensity scores)
- **Profiles store:** Stores and serves user profiles at low latency to your applications
- **Interventions**: Triggers real-time actions in your application based on user behavior

### Getting started

If you'd like to trial Signals, contact your Customer Success Manager or reach out to <support@snowplow.io>.

For detailed implementation guidance, see the [Signals documentation](/docs/signals/).